This article addresses the problem of identification of a nonlinear process operating over a wide range of conditions. The global space is divided into multiple local regimes, a nonlinear model is developed for each regime, and a quadratic programming-based algorithm is used to ensure smooth transition between the regimes on-line. The use of nonlinear models as opposed to linear models reduces the number of local regimes needed. Neural networks are used to model these regimes because of their strong ability to capture nonlinearity, and their combination with the switching algorithm improves transient performance. The performance of the method is demonstrated on an exothermic CSTR and a pH neutralization process.